R

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control. R is the base for many R packages listed in https://cran.r-project.org/

This software is also referenced in ORMS.


References in zbMATH (referenced in 3108 articles , 6 standard articles )

Showing results 1 to 20 of 3108.
Sorted by year (citations)

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  1. Tanaka, Kentaro: Conditional independence and linear programming (to appear) (2019)
  2. Abdi, Hervé; Beaton, Derek: Principal component and correspondence analyses using R (to appear) (2018)
  3. Agasisti, Tommaso; Ieva, Francesca; Paganoni, Anna Maria: Heterogeneity, school-effects and the north/south achievement gap in Italian secondary education: evidence from a three-level mixed model (2017)
  4. Albatineh, Ahmed N.; Boubakari, Ibrahimou; Kibria, B.M.Golam: New confidence interval estimator of the signal-to-noise ratio based on asymptotic sampling distribution (2017)
  5. Andersson, Björn; Wiberg, Marie: Item response theory observed-score kernel equating (2017)
  6. Antoine Filipovic-Pierucci, Kevin Zarca, Isabelle Durand-Zaleski: Markov Models for Health Economic Evaluations: The R Package heemod (2017) arXiv
  7. Arangala, Crista; Yokley, Karen A.: Exploring calculus. Labs and projects with Mathematica (2017)
  8. Arcagni, Alberto: On the decomposition by sources of the zenga 1984 point and synthetic inequality indexes (2017)
  9. Asar, Özgür; Ilk, Ozlem; Dag, Osman: Estimating Box-Cox power transformation parameter via goodness-of-fit tests (2017)
  10. Ashley Petersen, Noah Simon, Daniela Witten: SCALPEL: Extracting Neurons from Calcium Imaging Data (2017) arXiv
  11. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  12. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  13. Beckerman, Andrew P.; Petchey, Owen L.: Getting started with R. An introduction for biologists. (2017)
  14. Bezanson, Jeff; Edelman, Alan; Karpinski, Stefan; Shah, Viral B.: Julia: a fresh approach to numerical computing (2017)
  15. Bolstad, William M.; Curran, James M.: Introduction to Bayesian statistics (2017)
  16. Chakar, S.; Lebarbier, E.; Lévy-Leduc, C.; Robin, S.: A robust approach for estimating change-points in the mean of an $\operatornameAR(1)$ process (2017)
  17. Chaturvedi, Nimisha; de Menezes, Renée X.; Goeman, Jelle J.: A global $\times$ global test for testing associations between two large sets of variables (2017)
  18. Chen, Youhua: Impacts of phylogenetic dilution and concentration effects on species diversity and distribution patterns (2017)
  19. Cipolli, William III; Hanson, Timothy: Computationally tractable approximate and smoothed polya trees (2017)
  20. Dehmer, Matthias (ed.); Shi, Yongtang (ed.); Emmert-Streib, Frank (ed.): Computational network analysis with R. Applications in biology, medicine and chemistry (2017)

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Further publications can be found at: http://journal.r-project.org/